Category: Gen AI

An IVR system is extremely frustrating, lacks empathy, and is devoid of engagement. Hence, chatbots were introduced to redefine customer support in the last decade.

Since then, it has been some time now that these chatbots have started to replace the ages-old, traditional IVR systems but if you don’t see your chatbot function differently from an IVR, perhaps, a second thought should be given.

The key difference between a great chatbot and an old, boring IVR system is personalization, a truly competitive differentiator, defining user experience entirely in the world of customer support and management.

It brings the best of both human empathy and AI capability and thus, a perfect balance for customers. This not only makes users more comfortable while having a conversation but also ensures efficient and accurate solutions to thousands of queries at the same time.

Now that we have established what chatbots are and what they do, let’s dive into the features they use to understand & effectively solve user queries.

Here are features that Convert AI’s virtual sales agents use to seal the deal:

1. Understanding the Depth of the Conversation:

Powered by AI, Deep Learning, and Natural Language Processing, Oriserve’s chatbots are highly capable of understanding human conversations more than ever.

With the capability of intelligently understanding social media lingos, spelling errors, and sentence classification in multiple languages, chatbots have clearly started to blur the line between a human and a robot in having conversations.

Even if a user breaks a query into multiple sentences or adds multiple requests into one sentence, our chatbots can distinguish between these two and act accordingly.

Take the example of our Vodafone–Idea chatbot. If users ask to change their plans and recharge at the same time, our chatbot will intelligently show a catalog of available plans, and when a user selects one, it will redirect the users to a payment portal.

Plus, when the transaction is completed, our chatbot will also send the current payment status in the same chat.

2. Knows Precisely What Your Customers Want (‘Why’ and ‘How’, not just ‘What’):

Ori’s real-time engine computes customers’ behavior and delivers in-depth insights and patterns of customers’ likes, dislikes, most talked-about products, or wants, etc.

So, when a customer types a product keyword, based on these insights and patterns, along with the combination of metrics such as Seasonal Trends, Blending Products, Internal Research, and Multivariate Testings, the most suited products are recommended or the most appropriate resolution is provided, leading to better efficiencies, improving CSAT/NPS.

Hence, improving the core metric of CLTV.

3. Let Chatbots Analyze Sentiments to Act:

Sentiment analysis is crucial in creating the personality of a chatbot. It understands not only what the user is trying to say but also answers with a personal touch.

Ori’s AI-powered Sentiment Analyser Engine scans human behavior through texts, understands the situation the user might be in, accurately predicts the user’s emotions, and engages with personalized solutions.

Let’s take the below scenario as an example:

Customers often forget they have subscribed to Value Added Services such as Caller Tunes, and start charging at the chatbot furiously as soon as they notice a surge in their bill.

In this case, our chatbot will instantly understand the scenario and present the accurate reason for the surge.

In case our chatbot fails to answer appropriately, it can also hand the chat over to a live agent to tackle the situation.

4. Let a Live Agent Save the Day:

No chatbot is perfect, even the most powerful chatbots fail to understand complicated human conversations.

Hence, it is important to have someone behind the scenes to take control as and when a chatbot fails multiple times to solve anything.

Our chatbots are highly capable of understanding human emotions and conversations, but sometimes, they may fail to answer a few queries accurately. Hence, it has a failsafe to ensure users are not irritated when it fails more than once.

In case it fails to come up with correct answers to a question, it detects the same from users’ replies and sends an alert to a live agent to monitor the chat, and eventually take control over the chat as and when required, ensuring the satisfaction of the users while also keeping you in safe hands.

5. Offering Support in Multiple Languages:

Our chatbots, equipped with highly advanced AI integration, Deep Learning, and a set of robust algorithms, are capable of understanding what the users are saying in most of the languages globally such as, but not limited to, English, Hindi, Urdu, Tamil, Telugu, Malayalam, Bengali, Arabic, Cantonese, Spanish, Italian, French, German, Portuguese, Russian, Japanese, Korean, and solve the query efficiently in the same language.

Be it a query on order status or a question related to a product in a different language, our chatbots can present highly accurate results within microseconds.

Cutting the Long Story Short:

IVR is like a relic from the past. It is good but certainly not the best thing for the current environment.

On the other hand, chatbots are getting more and more advanced with each passing day. Who knows when a day might come when we will be talking to a bot so perfect that we fail to realize it is a bot and not a human.

Buying journey has changed because buyers are more tech-savvy, more empowered, and expect personal experiences at every touchpoint. It is increasingly challenging to reach decision-makers at the right time and engage them across multiple decision-making units in today’s world.

Today, marketing & sales practice is leveraging the digital ecosystem starting from identifying the right consumer to consumer retention by using technologies like behavior-based platforms, Data analytics, CRM, Automation, and AI as consumers are continuously evolving, and their consumption patterns are highly dynamic. Businesses are now leveraging social media, IoT, AI, artificial intelligence AI, and analytics to target and engage customers and help brands accelerate sales growth by using data-rich customer insights and intent-to-buy signals.

Organizations are leveraging the Next-gen technologies to create deeper connections with consumers. They offer conversational engagement through personalized offerings driving salespeople to have account-based strategies rather than adopting mass-selling opportunities to get a holistic view of the customer journey.

Understanding MarTech Stack 

A marketing technology stack is a grouping of technologies used to conduct and improve a brand’s marketing activities. Often, the focus of marketing technologies, aka “martech,” is to make complex processes more manageable, measure the impact of marketing activities, and drive more efficient spending. While martech might still be a new term to some, marketing technology is not.

Components of a MarTech Stack

When assembling a marketing technology stack, it’s essential to know which technologies are foundational and should be put in place first. Both B2C and B2B marketers should consider these technologies necessary as they will use different channels and techniques to acquire customers and will have varying technology needs as a result:

1. Content Management System (CMS): CMS is the technology that powers a website, blog, or other relevant web properties where businesses want to engage their customers.
2. Advertising technology: Advertising is a crucial customer acquisition technique for brands. Most brands often use a combination of SEM, display ads, retargeting, and ad tracking or attribution software.
3. Email: Email is a crucial customer communications channel that all brands need in their toolkit. Sometimes, email is a capability built into marketing automation or an inbound marketing platform.
4. Insights & Analysis: Brands should have access to their data to measure digital marketing activity. Most businesses will have website analytics and their business analytics tracked in either homegrown or third-party tools. A data warehouse can pull together data from various sources and make it easier to access. It can also deliver performance insights through content intelligence.
5. Experiential Marketing: Experiential marketing, also known as event marketing, is an essential aspect of marketing for many companies. With the growing popularity of virtual events, conferences and webinars, it’s crucial to have the right experiential marketing tools to manage these events.
6. Experience Optimization: Experience Optimizations include A/B testing and personalization software or programs that allow brands to take action on their analytics to make their marketing campaigns more efficient. 
7. Social Media: This includes technologies to monitor social activity and make social engagement easier, which can help maximize the impact of this marketing channel. Social networks such as LinkedIn and Facebook are also a vital part of the ads landscape, and many have paid advertising options available.
8. Digital Asset Management (DAM): Like a content management system, it stores content but typically focuses on keeping track of and authoring static assets like images, documents, and videos.
9. Customer relationship management (CRM): When CRM is used with a direct salesforce, marketing attribution can be tracked. CRMs track all customer relationships and can provide insights into how marketing campaigns influence sales pipelines and customer growth.
10. Search engine optimization (SEO): SEO is often an essential strategy for driving organic traffic to the website by ranking higher in search engines such as Google and often pairs well with content marketing strategy.

It’s also important to know which skill sets and team members businesses need to ensure that the marketing team benefits from the technologies. 

Here are a few categories to consider when thinking about how marketing technology will complement the business:

• Customer acquisition – website optimization, online marketing, event marketing, partner marketing
• Brand & communications – public relations, broadcast advertising, social media, sponsorships
• Product marketing – content marketing, product marketing, analyst relations
• Marketing operations – campaign performance, data analysis, and insight into marketing operations play a crucial role that will help enable the rest of the marketing team to make intelligent decisions with the marketing data.

Benefits of MarTech in Driving Sales

Marketing teams worldwide have long relied on these tools every day to consistently deliver relevant content to their audience. Martech also helps to:

1. Do More, Faster:

Use automated tools to save time by streamlining repetitive tasks such as pulling data, converting file formats, and finding assets.

2. Enhance Internal Communication:

Provide a way to communicate openly about project status and team goals to improve tracking.

3. Create Smarter Content:

Create more intelligent content by using insights and data to understand performance, optimize experiences, and deliver targeted content.

4. Build Better Relationships:

Strengthen customer and buyer relationships by knowing the right thing to say with easy access to past conversations, interactions, subscription information, and more.

5. Be Stronger Together:

The value of each tool is amplified when they all come together to reduce friction in workflows, allowing brands to work faster and wiser.

Building Marketing Technology Stack

Building a sustainable martech stack requires a strong understanding of the current and future technology landscape and where business needs fit into it.

Understanding tech gaps within the organization will also allow it to recognize opportunities for new tools as the need arises. Brands mapping their martech needs is vital in bringing clarity to the process. With six recognized categories in the martech landscape, it’s essential to review the tools and companies that make up each to understand how they could benefit the brand.

Gen-AI Sales Agents: Chatbots with AI to Drive Conversion from Website

Everyone knows selling online isn’t easy. Brands have to attract visitors and keep users happy throughout the purchase process. Adding to all of that is this year’s pandemic. But it’s not all doom and gloom. Despite these difficulties, a strategy in line with customer needs and the right technology can be a brand’s best ally.

A Harvard Business Review article says that companies using AI increased leads by 50%, reduced costs by 40% to 60%, and decreased customer call time by 60% to 70%. 

Let’s explain the main benefits of this technology and its main applications in sales strategy.

Driving quality traffic to the websites won’t help brands if they haven’t figured out ways to leverage them and achieve the desired conversion rates. If visitors are not converted into qualified leads, marketing investment returns will always be lower.

It is where Virtual AI sales agents can prove a great tool — brands can use them to devise a solid engagement strategy and drive the conversion. Therefore, brands should use AI-powered bots to automate the sales process and successfully engage and qualify website visitors. 

It is how chatbots for sales can ensure every dollar spent on marketing fetches better returns and certainly generates more revenues for businesses.

10 Sales Chatbot Features that Help Drive Conversions

Companies are increasingly using artificial intelligence to implement new strategies. Specifically, a study conducted by Forrester revealed that 46% of the companies implementing this technology today use it in their sales and marketing departments, and 40% in customer service.

Applied to chatbots, conversational AI lets brands automate customer communication points while still delivering a satisfying and efficient experience. This technology allows for natural conversations, understanding of regionalisms, interpretation of grammatical errors, and even usage of emojis, gifs, and memes. There are other advantages, like 24/7 availability, immediacy, and omnichannel service, to help a prospect throughout the entire purchase process, thus making them a partner at every step of the sales process.

Great customer conversations are essential to accelerating leads and growing conversions for any business. Companies are now delivering great experiences to customers via timely conversations and engaging them with the perfect message, boosting the sales pipeline thanks to AI chatbot marketing features.

To drive leads and increase conversions, businesses can utilize sales chatbot features in the following ways:

1. Pre-Qualify Leads 24×7:

Before talking to customers, brands have to get their attention. One way to do this is with chatbot advertisements. These are small buttons or images that stand out on websites that, when clicked, automatically open a chat window with a predetermined message.

This way, brands can bring users to the bot with a relevant message. Chatbots can be an excellent tool for optimizing the customer experience and boosting lead generation.

Since bots are conversational and always available, businesses can modify them based on the different stages of the customer journey and bolster the lead qualification effort.

However, businesses first need to build the chatbot for lead generation flow with relevant questions to ensure better customer engagement. It is also essential to consider the human touch in creating the bot flow to avoid sounding robotic, attract more visitors and generate more leads.

Best Practices to Qualify Leads Using Chatbots

• Consider the customer journey: Target the lead qualification bots based on the customer journey stage or the visitors’ specific page to get better results.
• Have step-by-step questions: Ensure there is a multi-step form system with specific questions for each stage to serve customers relevantly without confusing them.
• Prioritize the questions: Design the bot flow so that customers are asked the most critical questions on a priority basis, which boosts engagement.
• Place easy-to-click buttons: More leads can be qualified when the visitors are provided with buttons against questions, making the tasks easier for them.

2. Automate Appointment Bookings:

Reservations via chatbot are a favorite with customers because booking appointments based on availability is fun and interactive. They also like navigating through a tree of options, answering questions, and getting recommendations for bookings or reservations.

Businesses can automate appointment scheduling with chatbots and let visitors self-schedule. Additionally, this helps gather data to generate leads for the sales team. Chatbots can be customized or programmed to establish the time and duration of the appointment and allocate particular slots to consumers.

Further, integrating the chatbots with Google Calendar will help brands display the available slots to customers based on their time zone and prevent overbooking. The calendar integration will always ensure that both business and the lead receive a timely email invite so that the dates are remembered.

3. Assist Customers with Pricing Queries:

Through conversations, businesses can guide customers to find exactly what they’re looking for. When a user asks a question, the chatbot will direct them to the section where they can find the products related to what they asked. This prevents them from leaving the site before finding what they were looking for, thus improving retention.

Giving customers the pricing information at the right time can help ensure better leads and conversions. Chatbots can assist in automating the pricing process and save agents the tedious task of displaying it to visitors. More importantly, a Conversational AI-enabled chatbot can always handle customer queries efficiently and guide them with the correct product information through the sales funnel.

Tips for using chatbots to assist customers with pricing queries:

• Bot on the pricing page – Adding a bot on the pricing page can be incredibly helpful in answering routine questions and helping customers pick the right product quickly.
• Display product pricing – Brands can visually display product pricing and give customers a chance to compare prices for different products.
• Send product quotations to customers – Bots can automatically generate quotations or replace purchase order forms, boosting sales.

4. Ensure Online Orders From Social Media Pages:

Customers now expect online ordering systems to align with their social media usage, driving conversational eCommerce growth. Many businesses have started to add chatbots to social media pages enabling customers to place orders directly from official handles.

Twitter and Facebook Messenger users can now use a chatbot without placing an order over the phone.

Reasons to use chatbots for online ordering:

• Businesses can tap into the next generation of millennial customers by using chatbots for online orders on messaging platforms.
• The purchasing process becomes seamless for new-age customers as they don’t have to download an app to buy products from a brand.

5. Offer Product Recommendations:

Besides capturing leads and chatting with them, the chatbot can also be a great sales agent. Based on the correct content, it’ll be able to make personalized suggestions and be with the user through the entire process to help make the sale. It can even help sell more products through Up-Selling and Cross-selling techniques. They can suggest add-on products, creating a need the customer hadn’t initially thought of.

Most customers look for help to smoothly proceed through the final stage of the sales funnel. With quick answers to their queries and guidance, businesses can meet their preferences quickly. It is where customer service chatbots can prove extremely helpful as they can engage with customers via conversations, understand their needs better, and then recommend products accordingly.

These chatbots can efficiently guide users and eliminate the need to have a sales agent, and at the same time, buyers won’t need to waste their time searching and knowing about the products by themselves.

6. Give Personalized Welcome Messages:

Customers love to be greeted by sales agents because it makes them feel special. A gentle welcome at the start can always prove handy when engaging visitors on the website. Businesses can greet online customers with personalized messages and make an instant connection with them. A proactive chatbot can help enterprises engage customers of any type, whether new or returning ones and cater to their needs via conversations. Brands must focus on the chatbot design flow and prepare multiple messages tailored for a positive customer experience to be effective with personalized welcome messages.

7. Track the Status of Sales Order:

After buying a product, the customers expect prompt delivery. If a brand uses an old-fashioned order tracking system, the customer must manually find the shipping number to obtain the details. Integrating Conversational AI chatbots into the shipping system can help customers track their order status. Thus, buyers can be spared the inconvenience of calling the support agent or filling out the tracking order on a third-party website. 

8. Reduce Cart Abandonment Rate:

Cart abandonment is a big issue retailers and the eCommerce industry face worldwide. There could be many reasons why customers might leave the purchase mid-way, a complex checkout process, poor website navigation, or high shipping costs. AI chatbots can help reduce cart abandonment by filling the gaps between what customers want and want they should get. Businesses can adeptly use bots to trigger messages at various sales funnel stages and reduce cart abandonment rates.

How Chatbots Reduce Cart Abandonment

• Answer product FAQs: Chatbots can answer the FAQs before and during the purchase either in a clicked-on or automatically triggered manner and clarify customers’ doubts on time.
• Ensure Guided Selling: Brands can program and set up AI-powered chatbots to understand customer preferences quickly, answer their product queries, and automatically get triggered based on certain predefined conditions.
• Provide Last-Minute Personalized Offers: Businesses can use AI-based chatbots to build-up triggers like last-minute offers, discounts, something free with the products, etc., to reduce the cart abandonment rate.

9. Offer Multilingual Sales Support:

The relationship with the customer doesn’t end with the completion of sales. The important thing is that brands continue to offer 24/7 omnichannel service and personalization to answer questions about shipping status, changes, or returns. The customer can share the tracking number with the bot and immediately receive updates on the quality of their shipment. Bots can also send proactive messages on WhatsApp to notify the customer when their package will arrive. Most online shoppers prefer to make purchases in their language, and they would not go ahead if this option were not available.

Therefore, if a business is selling products across the globe, it will have customers with different choices for conversations. In such scenarios, AI chatbots can prove handy as they can be programmed to offer multi-lingual support and increase conversions. When brands can help with sales-related queries in multiple languages, they can quickly become more accessible and expand to new markets.

Benefits of Offering Multilingual Sales Support

• Improve customer experience – Using AI bots can help businesses engage customers better with faster and easier conversation in the native language and improve their experience with the brand.
• Expand customer base – A multilingual bot can help brands expand their reach, showcase the products to a new set of customers, and quickly move them down the sales funnel.

10. Build Lasting Relationships with Prospects:

To develop a long-term relationship and nurture it over time, brands can collect customer contact information through forms that can be transferred to the CRM software to add it to the newsletter, nurture or mailing lists.

Schedule a demo with our experts to know more.

Customer experience is now at the center of modern business strategies. Such a customer-centric approach is growing in our times due to massive competition and options available to consumers. But how do brands create a competitive edge and offer an exceptional customer experience?

AI allows us to hit that sweet spot and deliver impeccable sales as well as customer service. It enables consumers to get a hyper-personalized experience that is quick and efficient.

When you consider it from a customer’s perspective, creating a better experience is all about simplification. Conversational AI can help simplify the entire consumer experience from product understanding to purchase and from post-sales services to customer claims.

But in order to simplify the process, we also need to look at the pre-existing data and make the best use of it.

Behind Every Great AI is a Lot of Data

The more data that’s available, the more training it will have and the better it will perform. There’s a clear correlation between the amount of data that is used to bootstrap an AI’s knowledge base and its accuracy in carrying on conversations with your customers correctly.

To be accurate, data is the building block behind Conversation AI. Through the help of available data and patterns, AI defines the understanding of a customer’s message and looks for the desired information.

Let’s take an example here: Imagine that you’re creating a virtual agent, and the virtual agent sits on a website or landing page.

The website is going to be connected to a tech stack such as a CRM, Marketing automation tool, etc. Systems that will give you insight into historical data, historical and real time actions of a customer.

To know more on how you can use messaging platforms to capture first party customer data – view our blog piece here.

Maybe the user is on the returns page. Or they have added a product to the cart, or have just logged in – at each instance, they would want to talk about a different problem and have a different conversation. Several common scenarios may include:

• I recently purchased a shirt but I received the wrong colour.
• When will I get my package? I ordered a product from your site a few days ago!
• I tried to purchase a subscription for your platform. The amount is deducted but I still don’t have access. Help!

Now, they may connect with your sales assistants, who in return can recognize their phone number, understand what their previous interaction with the company was, and whether this conversation is likely to be relevant to a previous conversation.

If you smartly use the data that you already have on your customers, their data from their conversations, data on their preferences, based on previous customer interactions, or contextual, based on the actions that the customer has taken, then you’re able to provide truly personalized, truly contextual AI assistance that is going to create a superior customer experience.

Why You Should Be Concerned With The Quality Of Data?

While the quantity and availability of data is a necessity to train a Machine Learning model, it is equally important to have accurate, precise, and useful data.

Data that is inaccurately collected or extracted would make Conversational AI inefficient. As such poorly collected data can actually defeat the purpose of quick, efficient, and automated sales as well as customer support.

Therefore any solution that we build with Machine Learning has to use a verified and accurate set of data in order to be useful and improvised over time.

Quality Data In Conversational AI Can Lead to Numerous Performance Advantages:

• Good quality data can allow Conversational AI to fulfill any customer requests or questions accurately, creating a better experience for them. In turn, it helps brands gain customer loyalty. A win-win situation, right?
• Accurate data can allow you to be safe against false promises and inaccurate customer expectations.
• Efficient Conversational AI solutions can provide you with an idea of consumer interest and frequent concerns. This can help businesses to gain feedback on where and how they can improvise. Lending results beyond the solution itself.
• Machine Learning models can improve over time if it is fed the right data. Much like a positive feedback loop, such a Conversational AI solution would get better over time.
• Only high-quality data can give expected outcomes. Without a verified Conversational AI solution, there’s constant room for error and such a Machine Learning system must be supervised throughout the implementation.

In the End, It’s All About the Right Dataset

Gen-AI carries great potential to transform your customer experience. However, it is essential to understand that such a solution can only be beneficial to you if it is built upon the right dataset.

Schedule a demo with our experts today to know how we can help you with effective deployment of Gen-AI into your business.

Search ads use keywords to find products that match the user’s needs. When customers search for something and find a match, they might have questions that reduce dissonance and move them toward conversion. Moreover, the data from all those could help your business or client in the future too.

Landing Pages:

As a digital marketer, you might have read and implemented thousands of tactics to get your clients the conversion they want. One specific part of advertising – search ads – is a lot trickier than any type of social media ad. When working on a search engine ad and its copy, you need to focus on the landing page structure, the content, as well as the users, search intent vis-à-vis your product offering, or your ad will never perform.

After tons of practical layouts and strategies to optimize the post-click experience on landing pages, there is still a loophole. A majority of search ads do not convert, and customers often click and bounce.

If you are using the age-old techniques of copy alternatives or graphical representation, you may be losing out on personalized experiences. Today’s customers are expecting personalized, immersive, and context-rich experiences.

Post-Click Automation:

96% of paid Google Ads don’t convert. While most marketers use automation services, a flaw focuses on the pre-click phase. To move with the personalization towards the post-click phase, a smarter way is post-click automation (PCA).

What is Post Click Automation?

Marketing funnels have a post-click stage where automation is done through marketing technology, which is known as post-click automation. It is a technology through which marketers leverage advertisement conversions by providing one-to-one personalized experiences. It fills the gap between the pre-click segmentation and post-click reaction phases by combining elements like AI, Ad Mapping, Machine Learning, etc.

How Does It Impact the Post-Click Experience?

PCA combines all elements of post-click expertise like A/B testing, page creation, etc., to provide a comprehensive solution in a single place. Ad mapping, scalable creation, personalization, and optimization techniques are combined to create experiences for every stage of the advertising funnel for finding the search ad performance.

But even with post-click automation that claims to resolve the issues of bounce rates and low conversion via search campaigns and PPC, a complete solution is yet to be found.

What are the Customers Expecting?

If a customer searches for something online, they are looking ahead to find multiple options that fit their consideration set. With thousands of similar products and services, brands cannot rely simply on creating a landing page per ad. Customers have a wide range of options, which further lengthens their decision-making process, leading to their indecisiveness.

If you are relying on basic search and tactics, your CAC will take a toll. So, what is the way out?

Improve Post-Click Experiences Through Personalization Using AI & Conversations

Every customer wishes to engage with the brand they are likely to purchase from. While there are certain informative points mentioned, the customer might still be skeptical. The best way to approach the increasing bounce rates is to aid them with a technological product that converses with them at their time and convenience.

1. According to Salesforce, 69% of consumers prefer using chat interfaces as they deliver real-time answers and personalization. 
2. According to Business 2 Community, 82% of consumers claim that instant responses to their questions play a significant role in the buying process when contacting brands.

Engagement:

Considering how chatbots are impacting businesses, Adster aids your search ad with a conversational layer on the ad. Users can directly click on the Search Ad, and they will be redirected to the chat section where they can have a conversation.

The virtual sales agent answers their questions, thereby directly sharing an option of Buy Now and Payment thereafter. In this way, within a short period, you build brand value, recognition, aid product discovery, and drive revenue.

All that you have heard about the Zero Moment of Truth is finally here.

Forget building landing pages with content, graphics, and videos with CTA’s. With Conversational Search Ads – Adster, there is no need to collect data manually.

The customer engages with the brand directly without multiple touchpoints. It builds a virtual environment with immersive and personalized experiences without having to click on the landing page – in turn reducing landing page bounce rates.

The technology of Adster is patented, which allows it to dive into the user’s search intent and drive conversation accordingly. With this AI-driven mechanism, users can have a rich post-click experience without going through the hassle of reading landing page content.

Moreover, Conversational Search Ads help advertisers connect with customers at their time and capture the zero moments of truth. Finally, it also collects the PII, shares rich media for conversation, and collects payment on their behalf with end-to-end encryption and data privacy.

Re-Engagement in a Cookie-less World

Adster’s conversational Search Ads offer marketers and brand owners the ability to re-engage and win back customers via cookieless device-based re-engagement.

Through a well-crafted, context-relevant drip campaign, curated and personalized nudges can re-engage the user back into the buyer journey.

For brands looking to improve lifetime value, these re-engagement nudges can create seamless and memorable post-purchase experiences, accelerating top-of-mind and loyalty.

Video Synthesis Tech + Conversational AI = A Winning Formula

Post-click experiences and personalization can further be amplified using Video Synthesis technology. Using Video Synthesis technologies, brands can communicate with their customers, using videos offering an immersive and personalized experience. 

With Conversational AI and Video Synthesis, the buyers’ post-click experience can transform into a customized post-click journey.

Imagine this: A user searches for a product – clicks on a search ad – interacts with a Virtual Sales Agent (Adster) and is delivered a personalized brand video using Video Synthesis technology.

Through combining AdSter’s conversational search ads and re-engagement and Video synthesis – your brand re-engagement and retention can skyrocket to a whole new level.

Depending on the user’s stage in the sales funnel, a personalized video delivered via a cookieless re-engagement can effectively fast-track a user along the funnel to closure, directly impacting your brand’s bottom funnel and conversion metrics.

A recent example of this is the recently conducted #notjustacadbury ad campaign by Mondelez with Bollywood star Shahrukh Khan.

Using Video Synthesis tech, Mondelez is enabling customers to create advertisements for their local stores. Using AI, Machine learning, and Video Synthesis, users can create a personalized video with SRK by giving details about their store and category and SRK recreates the personalized ad by repurposing this information.

Transforming Sales Funnel

Conversational Search Ads bring a dynamic change in the sales funnel as instead of multiple touchpoints, you reduce it to a bare minimum of one or two. The entire length of your sales funnel shortens, which results in quicker action and faster revenue generation. Sourcing emails and sharing multiple emailers to convert? The Conversational AI bot will do all of that and more – through seamless API integrations with your MarTech stack.

To Conclude:

Landing pages are beneficial for search ads, but if you are looking to deliver real-time personalized and immersive experiences, adding conversational search ads to your marketing arsenal will accelerate your conversion metrics and fast-track your sales funnel.

Reduced bounce rates, lower customer acquisition costs, and cookieless personalized re-engagement are what you get with Adster Conversational Search Ads. Book a free demo with our experts to experience it yourself.

We are living in the golden age of Artificial Intelligence and Machine Learning and are seeing a multitude of applications in our everyday lives that have been touched by AI.

This invariably gets us thinking – how does AI work? How can I understand the nuances of AI?

Well, we put together a list of the top 10 books that you could read to improve your understanding of AI.

Many works have been published in the last two years that provide in-depth knowledge of the underlying concepts, technological methods, and applications of artificial intelligence. This list features books written by well-known computer scientists and practitioners who work in the AI field.

Whether you’re an AI/ML researcher, engineer, or business professional, you’re sure to discover a few fascinating books to add to your reading list this year.

1. Driven: The Race to Create the Autonomous Car – by Alex Davies

According to the “great man” theory, history is mostly shaped by heroes—big, brawny, brainy males (always dudes) who use brute power and intellect to create the future. Alex Davies, a WIRED graduate, has written a new book that debunks that old assumption. Davies delves into the history of self-driving cars and the zany, energetic ensemble of characters (still mainly males) who are attempting to bring the technology to fruition in Driven. As Davies explains, the dream can only be realized with the help of others. Until it doesn’t, that is. Then the lawsuits—and, in the instance of one engineer, handcuffs—fly.

Robot cars may one day transform how we live in the modern world. By 2050, autonomous vehicles might be worth $7 trillion, and multibillion-dollar corporations like Alphabet, GM, Ford, and Tesla are racing to iron out the wrinkles. AVs, on the other hand, were a scholarly curiosity around the turn of the century.

Then, in 2001, an obscure language in a financing bill directed government funds to the development of robot technology.

(A tip: keep an eye out for the native tortoises, who will pee on you if you try to relocate them when racing a robot across the desert.) This is a book for anyone who is tired with hero stories and wants to discover how the business of producing world-shaking robots really works.

2. If Then: How the Simulmatics Corporation Invented the Future – by Jill Lepore

Quick Quiz: Who was the first presidential contender to win a contested election by using computational modeling of the American electorate? You’re 56 years late if you said, Donald Trump.

The year was 1960, and John F. Kennedy had hired a little-known company named the Simulmatics Corporation to survey American voters, forecast their behavior, and provide campaign counsel using its groundbreaking “people machine.” Jill Lepore examines the early days of algorithmic behavior modeling and its eventual influence on politics and society in her history of Simulmatics (“the A-bomb of the social sciences,” a “Cold War Cambridge Analytica”).

Her portrayal of the 1960s acts as a mirror for 2020: America is in the midst of a racial justice revolt, a technical weapons race with its geopolitical adversary, and an election molded by technological influence. If one replaced “Simulmatics” with “Facebook” on any given page, the storyline would almost make sense. If Then tells the narrative of technology’s forefathers, whose work gave rise to the “people predictors” that shaped contemporary democracy and paved the way for today’s Silicon Valley behemoths.

3. Rebooting AI (2019) – by Gary Marcus and Ernest Davis

New York University professors Gary Marcus and Ernest Davis illustrate the technological and theoretical differences between successful AI that is confined by a set of rules (or a defined environment) and successful AI that can effectively engage with the complexity and nuances of an open world. This book is for researchers and businesspeople who wish to make concrete forecasts about AI’s near-term future. Ernest Davis is a Professor of Computer Science and Gary Marcus is a Professor of Psychology and Neural Science and the CEO of Robust.AI. This book, according to Noam Chomsky, is “lucid and highly knowledgeable, from a critical but sympathetic standpoint.

4. Applied Artificial Intelligence: A Handbook for Business Leaders – by Mariya Yao, Adelyn Zhou, Marlene Jia

For many, the commercial application of AI remains a baffling concept; however, Mariya, Adelyn, and Marlene lay down both the technical and business aspects of AI for individuals working in the field as well as those with a passing interest. This book is not an overabundance of technical material that many people will struggle to comprehend, but rather a step-by-step guide to identifying opportunities, assembling a diverse team, and conducting strategic experiments in the field, with all the information you’ll need.

5. Prediction Machines: The Simple Economics of Artificial Intelligence – by Ajay Agrawal, Joshua Gans & Avi Goldfarb

This 250-page primer on AI and Machines tackles the most common questions a newcomer to the business could have. Prediction Machines is an easy-to-understand book that checks all the boxes when it comes to demystifying the nature of AI and how it affects the economy today. Prediction Machines’ applicability and adoption at many levels of the economy will only improve as they become more common and inexpensive. If you want to understand how the employment market will change in the coming months and what management skills you should have to deal with the disruptions, this AI book could be a must-read for C-suite executives. The assessment is based on the present AI owners’ and users’ decision-making abilities.

6. The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity- by Byron Reese

The three key discoveries that eternally transformed civilization are fire, agriculture, and the wheel. Setting this theory in action, author Byron Reese compares AI to the Industrial Revolution, citing numerous examples of AI’s involvement in changing the curve. Byron is the founder and CEO of Gigaom.

In this fascinating, essential, and accessible exploration of the coming advances in robotics, computing, and related technologies from the publisher, of one of the world’s most popular technology news websites, Gigaom.com, learn about the next stage of humanity’s evolution—the age of artificial intelligence.

Byron Reese, says we’re developing robots that can perform human-like tasks. We’re working on creating computers that might be able to think for themselves. We’re probably on the verge of developing a new life form: a conscious computer to which we could delegate our thinking minds, together with a companion robot to execute our bodily functions. Reese tackles the impending technological revolution and its species-changing repercussions in enthralling and accessible language.

He aids us in comprehending both the practical and existential implications of this brave new world, resulting in a full description of what it means to be human.

7. Life 3.0 Being Human in the Age of Artificial Intelligence – by Max Tegmark

We are at the start of a new age, as Max predicted three years ago. Science Fiction, which was formerly just considered fiction, is rapidly becoming a reality. Over nearly 400 pages of information, the MIT professor, whose work has aided mainstream study on how to keep AI positive, separates myths from facts, utopias from dystopias, to investigate the next chapter of our lives. Are artificial intelligence systems safe? How can we expand it without robbing people of their sense of purpose? Should we be concerned about an autonomous weapons arms race? All of this and more is covered by Max.

8.Machine Learning Yearning – by Andrew Ng

Andrew Ng’s free e-book will show you how to best structure your Machine Learning projects. Andrew covers everything from ML system issue diagnosis to mismatched training sets, end-to-end learning, and transfer learning. For years, it has been suggested that the best way to make strategic decisions in Machine Learning is to enroll in a program/course where you will have direct contact with a mentor; however, this e-book eliminates the middleman in assisting you on your journey to becoming an educated individual in all things Machine Learning.

9. AI Superpowers: China, Silicon Valley, and the New World Order – by Kai-Fu Lee

This AI book was written by Kaifu Lee. AI Superpowers, a best-seller in the American market, naturally brings up the Chinese and AI white-collar sectors.

“PricewaterhouseCoopers believes that AI implementation will boost global GDP by $15.7 trillion by 2030. China is expected to take home $7 trillion of the total, roughly double the $3.7 trillion gained by North America.”: Kai-Fu Lee

Kai-Fu Lee, a former Google China CEO, and AI entrepreneur, has extensive first-hand knowledge of the Chinese AI movement. The book on AI covers the four waves of AI and the ongoing struggle for AI supremacy as well as the future of workplace automation:

• Internet AI
• Enterprise/Business AI
• Perception AI
• Autonomous AI

10. Data Mining: Practical Machine Learning Tools and Techniques – by Ian Witten, Eibe Frank, Mark Hall, and Christopher Pal

This book meets every expectation that you may have from a book on AI.

Data Mining: Practical Machine Learning Tools and Techniques is a handy book on data mining and analytics whether you’re taking a course in Python, Big Data Analytics, or SAS Analytics. It describes how Machine Learning algorithms operate for data mining and performance enhancement workflow. This book is presently accessible in English, as well as Chinese, German, and Korean translations. Based on customer feedback, we believe the writers are true experts in the field of Data Mining and Machine Learning, and that they have successfully communicated their expertise to readers by including authoritative presentations of modern data mining techniques.

These books will be valuable to anyone who has developed a career around AI and ML for progressing through the field and grasping some of the most sophisticated, practical, and pressing concerns that the field faces. These books are excellent resources for anyone looking to improve their technical abilities, productivity, or knowledge of the subject. Every week, a slew of new books on comparable subjects are produced and released.